A Python-Based Approach for Predictive Maintenance Condition Monitoring of Lubricants

Abstract

Effective condition monitoring of lubricants is crucial for ensuring optimal performance and reliability of machinery. This research paper presents a novel Python-based approach for predictive maintenance through the condition monitoring of lubricants. The proposed method utilizes Python programming language and its powerful data manipulation library, Pandas, to analyze and interpret lubricant data. By leveraging experimental values, particularly focusing on viscosity, the study aims to identify safe and unsafe conditions of lubricants. The developed Python-based solution enables efficient data processing, visualization, and analysis, providing valuable insights into the lubricant's condition. The results obtained from this approach can assist maintenance teams in making informed decisions, such as timely lubricant replacement or equipment maintenance, thereby minimizing the risk of equipment failure and maximizing operational efficiency. The proposed Python-based approach offers a practical and scalable solution for condition monitoring of lubricants, contributing to enhanced predictive maintenance strategies in various industries.